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Channel

Data Science

@Data_Science_Ai_Python

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

47,890subscribers

-196 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1001249354475
TypeChannel
Username@Data_Science_Ai_Python
CreatedBetween 1 March 2018 and 30 June 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live12 August 2026
Measurements held7
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/Data_Science_Ai_Python

Growth

47,89048,08647,9887 August 2026 — 48,086 subscribers7 August 2026 — 48,065 subscribers8 August 2026 — 48,038 subscribers9 August 2026 — 48,000 subscribers10 August 2026 — 47,965 subscribers12 August 2026 — 47,924 subscribers12 August 2026 — 47,890 subscribers7 August 202612 August 2026
7 measurements spanning 5 days, net -196. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 47,861–48,115 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 21:5347,890-34
12 Aug 2026, 00:2447,924-41
10 Aug 2026, 22:1147,965-35
9 Aug 2026, 23:3148,000-38
8 Aug 2026, 21:3048,038-27
7 Aug 2026, 23:3148,065-21
7 Aug 2026, 12:4548,086first reading

Engagement

7 posts held, back to 1 June 2025the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 16 pagesof Telegram’s post history, 20 posts per page.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 7 posts for this entry, the most recent from 20 February 2026. An engagement rate over an empty window would be a number about nothing.

Reaction mix

138 reactions across 7 posts, in 3 distinct kinds. The most used accounts for 77.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
10777.5%
👍2719.6%
💯42.90%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 7 of the 7 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 138reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 7 most recent posts we hold, published 1 June 2025 to 20 February 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Recent posts

20 Feb 2026, 15:38 UTC≈4,170 views15 reactionsread 12 August 2026
Forwarded from @Udemy7Photo

Data Science Courses 👨‍💻 Fresh picks covering Python + Pandas/NumPy, Machine Learning fundamentals, Data Visualization, plus trending topics like AI Agents, AI Security/Governance, and real-world tracks (finance & healthcare). ⚠️ Some courses have low spots, so they may expire soon #DataScience #Python #MachineLearning #Pandas #NumPy #Matplotlib #AI #AIAgents #MLOps #FreeCourses

13👍1💯1

17 Jan 2026, 14:19 UTC≈4,710 views16 reactionsread 12 August 2026
Forwarded from @Best_AI_toolsPhoto

🛫 Premium Courses+ 🎭 : #Courses 👄 : Multi-Language ⭐️ : 5.0 💬 Paid courses shared for free when available. Covers programming, design, business, and more.

13💯3

26 Dec 2025, 07:07 UTC≈6,390 views13 reactionsread 12 August 2026
Photo

Hi everyone 👋👋.My friends are here again for part 2 of our intro to Pandas👌🎉👏.In Pandas, you can easily extract more useful data points from existing data in the table, and because Pandas has been optimized to work on large amounts of data, column operations are super fast 💨..Here I divide the founders’ net worth by their age, to get a sense of their average wealth accumulation rate.Then I am interested to see who’s

12👍1

26 Nov 2025, 16:43 UTC≈7,350 views17 reactionsread 12 August 2026
Photo

Hi everyone 👋👋.I wanted to introduce Pandas to you in case it’s new to you. We will be working a lot with it in the future so a nice introduction will go a long way 🙌.I have asked a few of my friends ‼️ to help me introduce Pandas to you by showing up on the post 😂😂.Jokes aside, Pandas is a really powerful data analytics library in Python that I use almost everyday. It’s robust, fast, and great for prototyping data s

16👍1

24 Sept 2025, 09:51 UTC≈10,100 views23 reactionsread 12 August 2026
Photo

Being fluent in NumPy goes a long way in becoming a data scientist 🏃 Today we are taking an important step in that direction! 🚀 . Wanna know more? Check out the slides! . 👨‍💻#NumPy

15👍8

13 Aug 2025, 11:00 UTC≈12,300 views19 reactionsread 12 August 2026
Photo

Partitioning is an important technique when you have a large amount of data and like to partition it based on a pivot value. NumPy can do this very efficiently and it leads to some cool applications. . Wanna know more? Check out the slides! . . 👨‍💻#NumPy

11👍8

1 Jun 2025, 07:47 UTC≈16,400 views35 reactionsread 12 August 2026
Photo

Hi Data Science enthusiasts 👋 . Today, we are gonna talk about broadcasting in NumPy 🔢 . Broadcasting is a powerful, useful yet tricky feature in NumPy. If you know it well and use it intentionally, you can simplify a lot of code 👌 . However, if it’s used by mistake it can create bugs and a lot of headaches 🤕 . That’s because in NumPy, you can easily do operations between matrices even if they don’t have the same sha

27👍8

Showing the 7 most recent of 7 posts we hold for @Data_Science_Ai_Python. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Forward network

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

Republishes

Channels on the register whose posts this channel has forwarded.

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 12 August 2026 — this entry's latest reading, not the date you are reading this.

“Data Science” (@Data_Science_Ai_Python), 47,890 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/Data_Science_Ai_Python.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.